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\copyrightyear{2011}
\pubyear{2011}

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\title[LAILAPS search engine]{The LAILAPS search engine: an open
infrastructure for an integrative information retrieval} \author[Chen \textit{et~al}]{Jinbo Chen, Matthias Lange\footnote{to whom correspondence
should be addressed}\ \ and Uwe Scholz}
\address{Research Group Bioinformatics and Information Technology, IPK Gatersleben, D-06466 Gatersleben, Germany}

\history{Received on XXXXX; revised on XXXXX; accepted on XXXXX}

\editor{Associate Editor: XXXXXXX}

\maketitle

\begin{abstract}

\section{Summary:}
LAILAPS combines a keyword driven {\em search engine} for an integrative access to biomediacal databases, machine learning for a {\em content driven relevance ranking}, {\em recommender systems} for suggestion of related data records and query refinements with a {\em user feedback tracking system} for incremental relevance training. We present an installation package that is designed to deploy and configure a full features information retrieval portal for any kind and combination of biomedical databases. It is particularly valuable for intuitive data exploration and integration with a search engine inspired user interface and a relevance prediction system that considers individual user relevance criteria. Using well performing algorithms, data structures and indexing systems we are able to offer a search and ranking speed, which is in the range of seconds even for low cost hardware. This makes the system able to increase the efficiency of an integrative database exploration.

\section{Availability and Implementation:}
Source code and executable versions are available at: \href{http://lailaps.ipk-gatersleben.de}{http://lailaps.ipk-gatersleben.de}. The LAILAPS software is free for non-commercial use, the sample UniProt database files are copyright by the UniProt Consortium and are distributed under Creative Commons Attribution-NoDerivs License.
\section{Contact:} \href{lange@ipk-gatersleben.de}{lange@ipk-gatersleben.de}

\end{abstract}

\section{Introduction}

Type of search: Navigational, exploration -> siehe IR chapter oder Bucj Stock:
Information retrieval


Correct identification of causative genes for an important agronomic trait can
be very valuable for effective marker assisted breeding. Forward genetic
approaches such as linkage analysis and association mapping can determine
genomic regions (QTL) associated with a phenotype. However, even well-defined
QTL often span genomic regions that can contain hundreds of positional candidate
genes. Evaluation of potential functional candidates from such long lists is
often time-consuming and requires the integration of information from many
different sources such as gene function annotations, biochemical pathways, gene
expression data, comparative information from related organisms, gene knock-out
and over-expression and the scientific literature.  We will demonstrate how to
use the open-source Ondex framework (www.ondex.org) to integrate data gathered
from multiple databases into a semantically consistent knowledge network.
QTLNetMiner is a user-friendly web application that can interrogate plant and
animal knowledge networks and be used to show candidate genes and QTL associated
with given input terms (e.g. early flowering, disease resistance). The relevance
of a gene to particular query terms is weighted using information retrieval and
network inference methods. The supporting evidence networks for selected
candidate genes are visualized in the Ondex Web Java-applet. QTLNetMiner is
designed in a generic way and can be created for any organism with a sequenced
genome and an integrated Ondex knowledge network. We have currently developed
QTLNetMiner instances for Arabidopsis, poplar, potato, pig, cattle and chicken,
see http://ondex.rothamsted.ac.uk/QTLNetMiner/.



Search engines are an invaluable tool for retrieving information from the Web. In response to a user query, they return a list of results ranked in order of relevance to the
query. The user starts at the top of the list and follows it down examining one result at
a time, until the sought information has been found.

Phrases like ''Leveraging Data from Disperate Sources to Create Value'' or ''Turning the Data Deluge into Meaningfull Biological Knowledge''\footnote{From the announcement for the ''19th Annual Molecular Medicine Tri-Conference'' 2012 (\href{http://www.triconference.com}{http://www.triconference.com})} are characteristic metaphors describing the arise importance of ''data science'' as research area in bioinformatics. The wealth of scholarly knowledge and life science databases, the so called ''data deluge'' \citep{SweZanBes11}, is of significant importance for researchers in making scientific discoveries and health-care professionals in managing health-related matters. In contrast to the well established tools for biomedical textmining, the information acquisition over non-integrated databases driven by individual relevance ranking is becoming increasingly difficult \citep{Roo01}. Scholars in general and database queries in particular have to process huge amounts of data in a disciplined and efficient way. 
These data is spread among thousands of databases, which overlap in content, but differ substantially with respect to interface, formats and data structure \citep{Gal11}.

Bridging this gap by heading for Google, Entrez, PubMed or related search
engines \citep{Lu11} and information delivered as list of web pages or database entries are intuitive and popular methods \citep{DogMurNev09}, but show major drawbacks \citep{DivHeaWoo08}. Issues like finding most relevant information about the function of a protein, or identifying the protein that is involved in a certain activity of the cell cycle, are much more challenging tasks. One has the option to scan hundreds of biomedical databases and thousands of database records. Another option is to trust the ranking of the query results and consider only the top search results. In general, this is not fully satisfying, because the offered relevance ranking methods reaching from chronological order, query term frequency, hit position in the entry up to popularity based methods. The user may decide whether the suggested relevance rank is reliable or not, but he is very limited in incorporate his individual ranking criteria or the scientific knowledge domain into the particular data query portal \citep{
RicPraBri06}.+

%One of them is LAILAPS
%%with what we have been working
%\cite{13}. The key feature of LAILAPS is the relevance prediction,
%%. We use a relevance probabilistic prediction model using neural networks. To consider the fact
%%that data relevance is highly subjective to the user of an information retrieval system,
%%we support
%currently done with neural networks, whereby a specific neural network is trained per user, user profile or use case.
%%specific neural networks. In order to train a particular
%%neural network, we presented the network a reference set of 9-dimensional feature
%%vectors as input and for the output layer the related manual curated relevance score.
%%Each set consists of use case specific queries and the relevance rating of the query
%%results. Using reference sets of relevant ranked database entries, we trained the
%%different user profiles.


For example, the top-ranked Google hit for "{\em arginase}" refers to a Wikipedia entry\footnote{The term {\em arginase} was searched at November 16, 2011 using Google.com. The top ranked page was  \href{http://en.wikipedia.org/wiki/Arginase}{http://en.wikipedia.org/wiki/Arginase}}. This is because the page is referenced by a high number of web-pages or Google assigned a manual defined priority rank. But this does not consider the particular information need. One may be interested in medical relevant data for {\em arginase}, one may be interested in metabolic activity, one like to download a full length sequence of the encoding gene etc. Furthermore, the value of the scientific data quality of a data record is not exclusively depending by statistical features, like link count or query term frequency. In order to find scientific relevant database entries, scientists need strong scientific evidence in relation to the specific research domain and the users knowledge background. Criteria like who published in 
which journal, for which organism, evidence scores, surrounding keywords etc are of major importance. Even complete search guides are published, e.g. for dentists \cite{Day01}.

Those and more criteria where implemented as relevance models, which are used in several information retrieval systems. An brief summarizing overview of methods and systems can be found in \citep{JenSarBor06}.

Search engines have the potential of assisting in data retrieval from these \emph{structured} sources, but fall short of providing a relevance ranking of the results that reflects the needs of life science scholars. One such need is an information retrieval portal that supports
\begin{enumerate}
\item the seamless integration of any databases,
\item a recommendation system for refined search queries
\item suggestion of similar database entries
\item self learning relevance criteria
\item flexible to support different user pertinence criteria
\end{enumerate}

Those requirements where implemented in the LAILAPS system \citep{LanSpiCol10}. The key feature of LAILAPS is the relevance prediction. We use a relevance probabilistic prediction model using neural networks. To consider the fact that data relevance is highly subjective to the user of an information retrieval system, we support currently done with neural networks, whereby a specific neural network is trained per user, user profile or use case. specific neural networks. In order to train a particular neural network, we presented the network a reference set of 9-dimensional feature vectors as input and for the output layer the related manual curated relevance score. Each set consists of use case specific queries and the relevance rating of the query results. Using reference sets of relevant ranked database entries, we trained the different user profiles.

\section{LAILAPS User Interface}

\subsection{Querying and Suggestions}
synyonyms, edit distance, bloom filer
suffix strippuing: von MF Porter - ‎Zitiert durch: 5978 - ‎Ähnliche Artikel
An algorithm for suffix stripping. M.F. Porter. Computer Laboratory, Corn Exchange Street, Cambridge
\subsection{Scoring and Ranking}
features, machine learning
\subsection{Data Browsing and User Feedback}
\subsection{Recommender System}
similar data records TF-IDF

\subsection{Installation and Data Import}
\subsubsection{Data Loading}
\subsubsection{Indexing}
\subsubsection{Deployment}
%LogicServer, WebApplication, DBMS (ORACLE, H2)

\section{Implementation}
The LAILAPS search engine is written as three-tier J2EE application and is tested to run under the webserver TOMCAT, Oracle WebLogic and Jetty. It is based on LUCENE text indexing system and use H2 or ORACLE as storage backend. Source code, binary installation package, sample data files and a already deployed LAILAPS instances are available at the LAILAPS web site \href{http://lailaps.ipk-gatersleben.de}{http://lailaps.ipk-gatersleben.de}. The LAILAPS ranking logic and H2 database backend run currently on a 4 core processor Intel Pentium Xeon 2.1GHz, with 6GB of RAM and 480GB of solid state disk. The frontend is deployed at a Tomcat 6.0 server.
\subsection{Presentation Tier}
\subsection{Logic Tier}
\subsection{Data Tier}
The schema of the database is provided as Supplementary Material.
\input{sections/application.tex}
\section{Summary}
use case:

LAILAPS supports the knowledge discovery process . For example, it suggest the
hypothesis that the unigene OptiV1C05701 (GRMZM2G157323\_T01), which is used at
the 44K oligo chip for maize, and the Ensembl transcript id GRMZM2G426745 (maize
chromosome 5: 1,583,732-1,584,250 forward strand) play a role for the stress
response in maize to low nitrogen. This experimental validation is given in
(Schlüter et al., BMC Genomics, 14:442, 2013). The below workflow shows the
knowledge discovery process for this use case.

% du hattest im gehen nach dem Grund für die lailaps hypothese, dass die
% optimas unigene OptiV1C05701 mit stickstoffmangel in bezug gesetzt
% werden kann. Hier die informationskette:
% 
% annotation in Optimas:
% 
% OptiV1C05701  -> NP_01104849.1 -> Q9S7U8_MAIZE
% 
% protein in uniprot: Q9S7U8_MAIZE
% 
% gen: ZmRR2
% 
% Zusammenhang gene nitrogen response:
% 
% http://jxb.oxfordjournals.org/content/53/370/971.full.pdf


"In maize, expression of some nitrogen-responsive
genes, such as ZmRR1 and ZmRR2 encoding maize
response regulators, induced in leaves by the resupply
of nitrogen to nitrogen-depleted plants (Sakakibara
et al., 1998, 1999). In detached leaves, the effect can be
replaced by treatment with cytokinin, but not by inorganic
nitrogen sources (Sakakibara et al., 1998), suggesting that
the actual signal of the nitrogen availability is cytokinin(s). In the
whole plant, supplement of ammonium
ions to the nitrogen-depleted maize also induced the
ZmRR1 transcript, as is the case with nitrate ions
(Sakakibara et al., 1998). Although the changes in the
amounts of cytokinin species during ammonium administration have not
been determined, this result suggests
that supplement of ammonium ions also increased the
translocation of cytokinins to the leaf ..."
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\newline
{\em Conflict of Interest: none declared}



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\section*{Acknowledgement}
This work was supported by Institute of Plant Genetics and Crop Plant Research (IPK). Thanks to Thomas M{\"u}nch, Steffen Flemming for administration of the Web Server and providing virtual server. Thanks to Stephan Weise, Christian Colmsee and Markus Oppermann for exporting the databases {\tt MetaCrop}, the {\tt Genbank Information System}, the {\tt Garlic and Shallot Core Collection} and {\tt OPTIMAS}.
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